Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8811
Title: Multilevel memetic hypergraph partitioning with greedy recombination
Authors: Acikalin U.U.
Caskurlu B.
Keywords: memetic algorithms
multilevel hypergraph partitioning
Genetic algorithms
Applications domains
Database migrations
Hypergraph partitioning
Memetic
Memetic algorithms
Migration planning
Multilevel hypergraph partitioning
Multilevels
Partitioning problem
VLSI design
Benchmarking
Issue Date: 2022
Publisher: Association for Computing Machinery, Inc
Abstract: The Hypergraph Partitioning (HGP) problem is a well-studied problem that finds applications in a variety of domains. In several application domains, such as the VLSI design and database migration planning, the quality of the solution is more of a concern than the running time of the algorithm. In this work, we introduce novel problem-specific recombination and mutation operators and develop a new multilevel memetic algorithm by combining these operators with kKaHyPar-E. The performance of our algorithm is compared with the state-of-the-art HGP algorithms on 150 real-life instances taken from the benchmark sets used in the literature. The experiments reveal that our algorithm outperforms all others, and finds the best solutions in 112, 115, and 125 instances in 2, 4, and 8 hours, respectively. © 2022 Owner/Author.
Description: ACM SIGEVO
2022 Genetic and Evolutionary Computation Conference, GECCO 2022 -- 9 July 2022 through 13 July 2022 -- -- 181031
URI: https://doi.org/10.1145/3520304.3529050
https://hdl.handle.net/20.500.11851/8811
ISBN: 9.78145E+12
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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